US8468153B2 - Information service for facts extracted from differing sources on a wide area network - Google Patents
Information service for facts extracted from differing sources on a wide area network Download PDFInfo
- Publication number
- US8468153B2 US8468153B2 US12/691,687 US69168710A US8468153B2 US 8468153 B2 US8468153 B2 US 8468153B2 US 69168710 A US69168710 A US 69168710A US 8468153 B2 US8468153 B2 US 8468153B2
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/20—Services signaling; Auxiliary data signalling, i.e. transmitting data via a non-traffic channel
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2457—Query processing with adaptation to user needs
- G06F16/24578—Query processing with adaptation to user needs using ranking
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2477—Temporal data queries
Abstract
Description
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- a dynamic yearbook generator for Facebook that shows who dated who.
- an inference/correlation generated newspaper
- inference/correlation generated market data
- inference/correlation generated “most wanted
tsup_entity id | U5345435343453454456457568564 | ||
Name | IBM | ||
Type | Company | ||
URL | www.ibm.com | ||
Wikipedia | http://en.wikipedia.org/wiki/IBM | ||
Ticker | NYSE:IBM | ||
Total count | 123 | ||
| 15 | ||
Derivative 48 | 40 | ||
Total Rank | 0.345 | ||
| 0.8 | ||
Derivative 48 hours rank | 0.2 | ||
Aggregate rank | 0.524166667 | ||
tsup_extractor_entity_map:
tsup_entity id | U5345435343453454456457568564 |
extractor_entity id (IBM) | http://d.opencalais.com/comphash- |
1/7c375e93-de13-3f56-a42d- | |
add43142d9d1 | |
tsup_entity id | U5345435343453454456457568564 |
extractor_entity id (International | http://d.opencalais.com/comphash- |
Business Machines) | 1/6869b162-b816-3d1b-9711- |
d2273229c4fa | |
NOTE: first (master) associated extractor entity_id is used as the tsup_entity id.
-
- Each entity is given a ranking depending on either how “important” it is (indicated e.g. by how common it is in the database) or on the derivative of that importance (i.e. if it is has recently become more common).
- As a complement, the entity can be ranked within the entity's category—this makes more sense since it is hard to compare the importance of “New York” and “Britney Spears”.
- Entities can also be ranked according to more sophisticated measures, e.g. based on the sentiment/attitude towards an entity, and how that changes.
- An entity can also be boosted in ranking if it co-occurs with many other high-ranking entities (e.g. some previously unknown person meeting a lot of famous persons in Davos).
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- Each data source is given a credibility ranking; this ranking is domain dependant, e.g. a source can have high credibility in the finance domain but not in the technical domain.
- For each type of fact, such as financial, political, sports etc. the system retains and keeps up to date a list of sources that are deemed proxies for human interpretation of source credibility.
- A manual ranking is performed on 100 degree scale of the most common sources in the dataset. (Here, in contrast to ER, quantity is not the same as quality!). Future implementations will include feedback mechanisms to re-evaluate this ranking.
- Source Credibility can also be based on click-throughs, i.e. with what (relative) frequency users actually follow links to a certain source.
- For the blogosphere, rankings such as Spinn3r's ranking measures (based on inbound links, etc.) can be used in addition to a more subjective ranking of a source's credibility.
- In addition to source credibility, stream credibility can also be used (since a source such as WSJ will have different credibility for different RSS feeds
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- Each fact is given a ranking based on its source credibility and the ranking of the included entities.
- The IER is not static, but needs to be recalculated as source credibility can change (although slowly!) and entity ranking can change quite quickly.
-
- Each pair of facts has a proximity measure describing how “closely” they are being mentioned together. An initial approximation is that two facts have PM=1 if they occur in the same document, and otherwise PM=0. An fact always has
proximity measure 1 to itself. - Other properties that yield a high proximity measure is co-location and closeness (or overlap) in time; however, it is important to keep PM and SM separate. Closeness in time and space needs to be computed using a hierarchy of spacial entities and time intervals.
- Proximity is transitive, but with a fading factor. For example, if PM(a,b)=0.5 and PM(b,c)=0.3 then PM(a,c)=0.15F, where F is a Fading Factor.
- For practical reasons, a threshold T is generally needed, such that if PM<T then PM is handled as 0 (thus breaking the “transitive chain” of proximity).
- Each pair of facts has a proximity measure describing how “closely” they are being mentioned together. An initial approximation is that two facts have PM=1 if they occur in the same document, and otherwise PM=0. An fact always has
-
- Each pair of facts has a similarity measure describing how similar they are. The similarity measure is calculated based on if the two facts are of the same type, if they relate to the same entities and if they are close/overlapping in time. A fact always has
similarity measure 1 to itself. - As for PM, similarity has to be computed by taking into account entity hierarchies. For example, Stockholm is more similar to Göteborg than to New York, since Stockholm and Göteborg are both in Sweden. This can become tricky—for example, Malmö is more similar to Köpenhamn than to Stockholm because Köpenhamn is closer—but in a different country.
- Each pair of facts has a similarity measure describing how similar they are. The similarity measure is calculated based on if the two facts are of the same type, if they relate to the same entities and if they are close/overlapping in time. A fact always has
-
- The derived ranking of an fact is calculated based on its Initial Event Ranking and on the ranking of all other facts, using the Proximity and Similarity Measure. Initial Event Ranking is determined as follows.
Initial Event Ranking
- The derived ranking of an fact is calculated based on its Initial Event Ranking and on the ranking of all other facts, using the Proximity and Similarity Measure. Initial Event Ranking is determined as follows.
IER(E)=f(c(S),g(r(e 1) . . . r(e n)))
The functions c, f and g all have the value range 0 . . . 1.
There are many ways to choose the functions f and g; one choice is to multiply the source credibility with the aggregated entity rankings, i.e. f(x,y)=x*y. The entity weight aggregation function g can be chosen e.g. as the maximum ranking of any included entity, or the mean ranking. Other aggregation functions are of course also possible.
Derived Event Ranking
DER0(e)=IER(e)
IER(A)=4.0
IER(B)=2.0
IER(C)=4.0
IER(D)=1.0
Furthermore, assume that there is one SM>0 (apart from entities being fully similar to themselves!)
SM(B,C)=0.8
This is a high degree of similarity—for example the same event type and same entities but different times. This example is shown graphically in
The DERA function converges after 29 iterations with the following values:
DERA 29(A)=4.0
DERA 29(B)=3.2
DERA 29(C)=4.0
DERA 29(D)=1.0
So, event B has been given a higher ranking than its IER, and all other rankings remain unchanged.
IER(A)=4.0
IER(B)=2.0
IER(C)=4.0
IER(D)=10.0
SM(B,C)=0.8
SM(C,D)=0.5
In this case, the iterative method converges after 34 iterations with the following result:
DERA 34(A)=4.0
DERA 34(B)=4.0
DERA 34(C)=5.0
DERA 34(D)=10.0
So, both B and C have now been boosted, although C has not been boosted so much since the similarity to the highly ranked D is not so big.
Proof Sketch that DERA Always Converges
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- 1. There is always one or more events with a DERA value higher than all others. These will never be boosted by DERA
- 2. For an event e the DERA value will converge when its DERA value is greater than or equal to
- DERA n(ei)*SM(e,ei) for all its neighbours ei. Since DERA n+1(e)>=DERA n(e) for all n, then the iterative method will converge.
- Note: the number of iterations needed for convergence is dependent on the maximum diameter of the graph where events are nodes and SM-relationships edges.
Using Both Similarity And Proximity—The DERB Method
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- Compute the current tsup_entity total count, i.e. the total number of occurrences of all related extractor_entities. Add this to the queue of tsup_entity counts, tagged with CT. (NOTE: this can be “infinite” or of a maximal size equivalent to the maximal window size for computing derivatives that it is desired to allow (see below).
tsup_entity Ranking
- Compute the current tsup_entity total count, i.e. the total number of occurrences of all related extractor_entities. Add this to the queue of tsup_entity counts, tagged with CT. (NOTE: this can be “infinite” or of a maximal size equivalent to the maximal window size for computing derivatives that it is desired to allow (see below).
-
- Compute tsup_entity derivates based on count changes over different window sizes, e.g. 2, 48 hours=when N=30
minutes 4 and 96 steps back in the total count queue. - NOTE: several different difference windows could be used.
- Compute tsup_entity derivates based on count changes over different window sizes, e.g. 2, 48 hours=when N=30
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- Find maximal total count and derivative 2/48 in category.
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- Compute normalized ranks (divide by max in category) for total count and each derivative.
- Compute aggregate tsup_entity ranking from normalized ranks: aggregate_rank=(total_rank+2*deriv48_rank+3*deriv2_rank)/6
- Store all four rank values for entity
- NOTE: this is just one possible aggregation function—different ones can be used.
- NOTE: the ranking could alternatively be recalculated each time a new entity reference is detected.
Initial Event Ranking
For each fact:
IER_mean=sc*sum(er 1 . . . er n)/n
IER_max=sc*max(er 1 . . . er n)
Claims (22)
Priority Applications (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US12/691,687 US8468153B2 (en) | 2009-01-21 | 2010-01-21 | Information service for facts extracted from differing sources on a wide area network |
US13/920,826 US20150019544A1 (en) | 2009-01-21 | 2013-06-18 | Information service for facts extracted from differing sources on a wide area network |
US17/320,132 US20220292103A1 (en) | 2009-01-21 | 2021-05-13 | Information service for facts extracted from differing sources on a wide area network |
Applications Claiming Priority (2)
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US20556709P | 2009-01-21 | 2009-01-21 | |
US12/691,687 US8468153B2 (en) | 2009-01-21 | 2010-01-21 | Information service for facts extracted from differing sources on a wide area network |
Related Child Applications (1)
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US13/920,826 Continuation US20150019544A1 (en) | 2009-01-21 | 2013-06-18 | Information service for facts extracted from differing sources on a wide area network |
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US20100299324A1 US20100299324A1 (en) | 2010-11-25 |
US8468153B2 true US8468153B2 (en) | 2013-06-18 |
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US12/691,687 Active US8468153B2 (en) | 2009-01-21 | 2010-01-21 | Information service for facts extracted from differing sources on a wide area network |
US13/920,826 Abandoned US20150019544A1 (en) | 2009-01-21 | 2013-06-18 | Information service for facts extracted from differing sources on a wide area network |
US17/320,132 Pending US20220292103A1 (en) | 2009-01-21 | 2021-05-13 | Information service for facts extracted from differing sources on a wide area network |
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US13/920,826 Abandoned US20150019544A1 (en) | 2009-01-21 | 2013-06-18 | Information service for facts extracted from differing sources on a wide area network |
US17/320,132 Pending US20220292103A1 (en) | 2009-01-21 | 2021-05-13 | Information service for facts extracted from differing sources on a wide area network |
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Cited By (8)
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US20160034565A1 (en) * | 2014-07-29 | 2016-02-04 | International Business Machines Corporation | Managing credibility for a question answering system |
US9495358B2 (en) | 2006-10-10 | 2016-11-15 | Abbyy Infopoisk Llc | Cross-language text clustering |
US9626358B2 (en) | 2014-11-26 | 2017-04-18 | Abbyy Infopoisk Llc | Creating ontologies by analyzing natural language texts |
US9626353B2 (en) | 2014-01-15 | 2017-04-18 | Abbyy Infopoisk Llc | Arc filtering in a syntactic graph |
USRE46902E1 (en) | 2013-06-25 | 2018-06-19 | Jpmorgan Chase Bank, N.A. | System and method for customized sentiment signal generation through machine learning based streaming text analytics |
US10956427B2 (en) | 2016-07-18 | 2021-03-23 | Bioz, Inc. | Continuous evaluation and adjustment of search engine results |
US11012448B2 (en) | 2018-05-30 | 2021-05-18 | Bank Of America Corporation | Dynamic cyber event analysis and control |
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US8060463B1 (en) * | 2005-03-30 | 2011-11-15 | Amazon Technologies, Inc. | Mining of user event data to identify users with common interests |
US9710556B2 (en) * | 2010-03-01 | 2017-07-18 | Vcvc Iii Llc | Content recommendation based on collections of entities |
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US8392431B1 (en) * | 2010-04-07 | 2013-03-05 | Amdocs Software Systems Limited | System, method, and computer program for determining a level of importance of an entity |
US8725754B2 (en) * | 2011-05-16 | 2014-05-13 | Sridhar Gopalakrishnan | Method and system for modeling data |
US10223451B2 (en) | 2011-06-14 | 2019-03-05 | International Business Machines Corporation | Ranking search results based upon content creation trends |
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US9626358B2 (en) | 2014-11-26 | 2017-04-18 | Abbyy Infopoisk Llc | Creating ontologies by analyzing natural language texts |
US11281678B2 (en) | 2016-07-18 | 2022-03-22 | Bioz, Inc. | Continuous evaluation and adjustment of search engine results |
US11768842B2 (en) | 2016-07-18 | 2023-09-26 | Bioz, Inc. | Continuous evaluation and adjustment of search engine results |
US10956427B2 (en) | 2016-07-18 | 2021-03-23 | Bioz, Inc. | Continuous evaluation and adjustment of search engine results |
US11012448B2 (en) | 2018-05-30 | 2021-05-18 | Bank Of America Corporation | Dynamic cyber event analysis and control |
Also Published As
Publication number | Publication date |
---|---|
US20220292103A1 (en) | 2022-09-15 |
US20150019544A1 (en) | 2015-01-15 |
US20100299324A1 (en) | 2010-11-25 |
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